iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism

Whole-body mass spectrometry imaging reveals the systemic metabolic disorder and catecholamines biosynthesis alteration on heart-gut axis in heart failure rat

INTRODUCTION

Heart failure (HF) is a systemic metabolic disorder disease, across multiorgan investigations advancing knowledge of progression and treatment of HF. Whole-body MSI provides spatiotemporal information of metabolites in multiorgan and is expected to be a potent tool to dig out the complex mechanism of HF.

OBJECTIVES

This study aimed at exploring the systemic metabolic disorder in multiorgan and catecholamines biosynthesis alteration on heart-gut axis after HF.

METHODS

Whole-body MSI was used to characterize metabolic disorder of the whole rat body after HF. An integrated method by MSI, LC-MS/MS and ELISA was utilized to analyze key metabolites and enzymes on heart, small intestine, cecum and colon tissues of rat. Gut microbiota dysbiosis was investigated by 16S rDNA sequencing and metagenomic sequencing. Validation experiments and in vitro experiments were performed to verify the effect of catecholamines biosynthesis alteration on heart-gut axis after HF.

RESULTS

Whole-body MSI exhibited varieties of metabolites alteration in multiple organs. Remarkably, catecholamine biosynthesis was significantly altered in the serum, heart and intestines of rats. Furthermore, catecholamines and tyrosine hydroxylase were obviously upregulated in heart and colon tissue. Turicibacter_sanguinis was relevant to catecholamines of heart and colon. Validation experiments demonstrated excessive norepinephrine induced cardio-intestinal injury, including significantly elevating the levels of BNP, pro-BNP, LPS, DAO, and increased the abundance of Turicibacter_sanguinis. These alterations could be reversed by metoprolol treatment blocking the effect of norepinephrine. Additionally, in vitro studies demonstrated that norepinephrine promoted the growth of Turicibacter_sanguinis and Turicibacter_sanguinis could import and metabolize norepinephrine. Collectively, excessive norepinephrine exerted bidirectional effects on cardio-intestinal function to participate in the progression of HF.

CONCLUSION

Our study provides a new approach to elucidate multiorgan metabolic disorder and proposes new insights into heart-gut axis in HF development.

Mass spectrometry imaging for spatially resolved multi-omics molecular mapping

The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop

Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

Protocol for metabolic synergy analysis of multi-organs in mice using iMS2Net

Biomolecule regulation and communication between cells and organs show specific network and regional features and play an important role in disease progression. Here, we describe steps for in situ detection of biomolecules with detailed spatial distribution using imaging mass spectrometry (iMS). Using the information on inter-cells and inter-organs metabolic interactions provided by iMS, we detail the establishment of an iMS-dataset-sourced multiscale network strategy and present steps for exploring metabolic responses in environmental-exposure-induced disease model. For complete details on the use and execution of this protocol, please refer to Dong et al. (2022).¹.

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.